Title:	Mutual Support of Data Modalities in the Task of Sign Language Recognition
Other Titles:	Vzájemná podpora datových modalit v úloze rozpoznávání znakového jazyka
Authors:	Gruber, Ivan Krňoul, Zdeněk Hrúz, Marek Kanis, Jakub Boháček, Matyáš
Citation:	GRUBER, I. KRŇOUL, Z. HRÚZ, M. KANIS, J. BOHÁČEK, M. Mutual Support of Data Modalities in the Task of Sign Language Recognition. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Nashville: IEEE, 2021. s. 3419-3428. ISBN: 978-1-66544-899-4 , ISSN: 2160-7508
Issue Date:	2021
Publisher:	IEEE
Document type:	konferenční příspěvek ConferenceObject
URI:	2-s2.0-85116062397 http://hdl.handle.net/11025/47308
ISBN:	978-1-66544-899-4
ISSN:	2160-7508
Keywords:	rozpoznávání znakového jazyka;konvoluční neuronové sítě
Keywords in different language:	sign language recognition;convolutional neural networks
Abstract in different language:	This paper presents a method for automatic sign language recognition that was utilized in the CVPR 2021 ChaLearn Challenge (RGB track). Our method is composed of several approaches combined in an ensemble scheme to perform isolated sign-gesture recognition. We combine modalities of video sample frames processed by a 3D ConvNet (I3D), with body-pose information in the form of joint locations processed by a Transformer, hand region images transformed into a semantic space, and linguistically defined locations of hands. Although the individual models perform sub-par (60% to 93% accuracy on validation data), the weighted ensemble results in 95.46% accuracy.
Rights:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © IEEE
Appears in Collections:	Konferenční příspěvky / Conference Papers (KKY) Konferenční příspěvky / Conference Papers (KKY) OBD

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